Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,080,159. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim limitations of the instant application are present in the more detailed patent ‘159 as shown in the mapping below.
Regarding claim 1, ‘159 discloses a method to detect intersection crossings by a vehicle (claim 1 lines1-2), the method comprising: receiving, by one or more processors coupled to a memory, a sequence of frames captured by an image capture device mounted to the vehicle (claim 1 lines 3-5); generating, by the one or more processors, an intersection status data structure for each frame in the sequence of frames using a classification model trained on a labeled dataset of frames depicting roadways, wherein each label of the labeled dataset used to train the classification model is applied to a substantial portion of an image frame (claim 1 lines 6-12); classifying, by the one or more processors, using an object detection model, a set of features detected in each frame of the sequence of frames (claim 1 lines 13-15); and detecting, by the one or more processors, an intersection crossing event based on the intersection status data structure of each frame and the classification of each of the set of features detected in each frame (claim 1 lines 16-19).
Claims 2-10 are similarly analyzed and mapped to claims 2-20 of ‘159.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claims 1-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sajjadi Mohammadabadi et al US 2020/0293796 (hereinafter “Sajjadi Mohammadabadi”, cited in the IDS).
Sajjadi Mohammadabadi discloses a method to detect intersection crossings by a vehicle (see paragraph 0021, systems and methods are disclosed related to intersection detection and classification in autonomous machine applications), the method comprising: receiving, by one or more processors coupled to a memory, a sequence of frames captured by an image capture device mounted to the vehicle (see paragraph 0035, the sensor data may include image data representing an image(s), image data representing a video, etc. as seen in figure 5 the sensor data is input to a machine learning model, which is carried out using a processor, see paragraph 0065) ; generating, by the one or more processors, an intersection status data structure for each frame in the sequence of frames using a classification model trained on a labeled dataset of frames depicting roadways, wherein each label of the labeled dataset used to train the classification model is applied to a substantial portion of an image frame (the sensor data may applied to a neural network such as deep neural network "DNN" that is trained using simulated data (labeled dataset of frames) to identify areas of interests (intersection status data) pertaining to intersection such as raise pavement markers, sidewalks, cross-walks, turn-off, etc.; para [0025], [0034]-[0035, note that the term “substantial” is subjective, as seem in figure 2a a substantial portion of the image is included in the bounding box 204 ]); classifying, by the one or more processors, using an object detection model, a set of features detected in each frame of the sequence of frames (determining information about objects detected in the sensor data; para [0095], [0102]-[0103]); and detecting, by the one or more processors, an intersection crossing event based on the intersection status data structure of each frame and the classification of each of the set of features detected in each frame (detecting an object crossing an intersection based on the areas of interest and information about the objects detected in the sensor data; Fig.2A-2B, 3A-3B, para [0041]-[0042], [0072]-[0073], [0102]).
Regarding claim 2, Sajjadi Mohammadabadi discloses wherein the object detection model comprises a neural network that comprises at least one layer trained for detecting an object in the image frame (see figure 4 step B412 and paragraph 0056).
Regarding claim 3, Sajjadi Mohammadabadi discloses wherein the classification model comprises a neural network that comprises at least one layer trained for detecting a traffic light in the image frame (see figure 4 step B412, paragraph 0056, and paragraph 0095).
Regarding claim 4, Sajjadi Mohammadabadi wherein the classification model comprises a plurality of output heads corresponding to characteristics of the intersection in the image frame (see paragraph 0038 which discloses the areas of interest within the intersection include a number of attributes which are interpreted as output heads corresponding to characteristics of the intersection).
Regarding claim 5, Sajjadi Mohammadabadi discloses wherein at least two of the plurality of output heads are trained on a common set of training data (see paragraph 0038).
Regarding claim 6, Sajjadi Mohammadabadi discloses wherein the plurality of output heads comprises at least one of an intersection type, a crossing type, a first status of the traffic light for a left turn, a second status of the traffic light for a right turn, or a third status of the traffic light for going straight through the intersection (see paragraph 0039, the number of attributes may correspond to the types of features e.g. Intersections that the machine learning model is trained to predict).
Regarding claim 7, Sajjadi Mohammadabadi discloses wherein the one or more processors execute logic combining a classification model output of an intersection crossing event based on the intersection status data structure of each frame and an object detection model output of the classification of each of the set of features detected in each frame (see paragraphs 0038-0039).
Regarding claim 8, Sajjadi Mohammadabadi discloses inputting, by the one or more processors, the sequence of frames into an object detection layer and a traffic light detection layer at a same time (see paragraph 0038, attributes are determined at the same time).
Regarding claim 9, Sajjadi Mohammadabadi wherein each label comprises an indication of whether the vehicle is approaching an intersection, within an intersection or not at an intersection (see paragraph 0078 determining when approaching an intersection).
Claim 10 is similarly analyzed to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached 892 notice of references cited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN B STREGE whose telephone number is (571)272-7457. The examiner can normally be reached M-F 9-5 (PST).
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/JOHN B STREGE/ Primary Examiner, Art Unit 2669
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